Intelligent marketing using group presence

ABSTRACT

Methods, computer program products, and systems are presented. The methods include, for instance: automatically selecting an optimal marketing collateral for a location in a venue based on a group of a patron and a relationship of the group and notifying the patron with the optimal marketing collateral such that the patron may utilize the optimal marketing collateral.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/299,528, filed Oct. 21, 2016, entitled, “Intelligent Marketing UsingGroup Presence”, the entirety of which is hereby incorporated herein byreference.

TECHNICAL FIELD

The present disclosure relates to automated marketing campaign, and moreparticularly to methods, computer program products, and systems fornotifying patrons with product marketing information as intelligentlyselected based on company of the patrons.

BACKGROUND

Conventionally, in-store marketing campaigns indiscriminately providevarious marketing information to patrons in a store. Marketing effortsfor specific products are often exercised in the vicinity of thespecific products to promote sales by informing the patrons with variouspromotions in which the patrons may be interested, such as specialdiscounts and bundle offers of the specific products.

SUMMARY

The shortcomings of the prior art are overcome, and additionaladvantages are provided, through the provision, in one aspect, of amethod. The method for intelligent marketing includes, for example:detecting, by one or more processor, a location event for a locationwithin a venue as generated by a patron; determining a group of thepatron and a relationship of the group; selecting an optimal marketingcollateral for the patron based on the group and the relationship fromthe determining; and communicating the optimal marketing collateral fromthe selecting to the patron such that the patron may utilize the optimalmarketing collateral.

Additional features are realized through the techniques set forthherein. Other embodiments and aspects, including but not limited tocomputer program product and system, are described in detail herein andare considered a part of the claimed invention.

BRIEF DESCRIPTION OF THE DRAWINGS

One or more aspects of the present invention are particularly pointedout and distinctly claimed as examples in the claims at the conclusionof the specification. The foregoing and other objects, features, andadvantages of the invention are apparent from the following detaileddescription taken in conjunction with the accompanying drawings inwhich:

FIG. 1 depicts a system 100 for intelligent marketing based on grouppresence, in accordance with one or more embodiments set forth herein;

FIG. 2 depicts a flowchart performed by the intelligent marketingengine, in accordance with one or more embodiments set forth herein;

FIGS. 3A, 3B and 3C depict examples of marketing collaterals presentedfor a patron in various group scenarios, in accordance with one or moreembodiments set forth herein;

FIG. 4 depicts a cloud computing node according to an embodiment of thepresent invention;

FIG. 5 depicts a cloud computing environment according to an embodimentof the present invention; and

FIG. 6 depicts abstraction model layers according to an embodiment ofthe present invention.

DETAILED DESCRIPTION

FIG. 1 depicts a system 100 for intelligent marketing based on grouppresence, in accordance with one or more embodiments set forth herein.

The system 100 includes an intelligent marking engine 110, whichreceives patron data 109 originated from a patron amongst one or morepatron 101 and sends an optimal marketing collateral 189 to the patron101 in a venue. In one embodiment of the present invention, the venuemay be a retail store. The intelligent marketing engine 110 selects theoptimal marketing collateral 189 from marketing collaterals 157 ofmarketing campaign data 150, based on an observation that when thepatron 101 is in a group, the patron 101 may behave differently inmaking purchase decisions for certain products according to whether ornot the patron 101 is shopping in a group as well as which type of groupthe patron 101 is presently shopping with, etc. For example, the patron101 may be expected to purchase a respectively distinctive product froma frozen food section of the store when the patron 101 is shoppingwithout a company, with one or more family member, with one or morefriend, social acquaintance, or colleague. In one embodiment of thepresent invention, the optimal marketing collateral 189 may be a pushednotification on a mobile device of the patron 101. By delivering theoptimal marketing collateral 189 of a product that the patron 101 ismost likely to purchase in a certain section of the store based onanalyzing group presence and group dynamics of the patron 101 as noted,the intelligent marketing engine 110 may effectively promote the productwithout overwhelming the patron 101 with numerous promotional messagesor without annoying the patron 101 with a promotional notification on aproduct with which the patron 101 is uninterested, as in conventionalmarketing.

The patron data 109 may be generated by use of various monitoringdevices in a store, or a generic mobile device on the patron 101 such asa smartphone, etc. Examples of the monitoring devices in the store mayinclude, but are not limited to, devices identifying the patron 101 suchas video cameras and image recognition, radio frequency identification(RFID) tags and scanners, and various micro-location devices within thestore by use of individual mobile devices on the patron 101 andcorresponding data collection systems based on Bluetooth®, Wi-Fi®, nearfield communication (NFC), etc. (Bluetooth is a registered trademark ofBluetooth Special Interest Group in the United States and othercountries; Wi-Fi is a registered trademark of Wi-Fi Alliance in theUnited States and other countries) Further, data stored in a genericmobile device such as a smartphone on the patron 101 may be utilized toidentifying the patron 101 and determining group relations of the patron101, as consented by the patron 101 beforehand such as signing up forstore membership/reward program by downloading mobile app, etc.

The patron data 109 may include location data within the store as setfor marketing purposes indicating if the patron 101 enters or exits aspecific product section of the store, how long the patron 101 stays inthe specific product section of the store, if the patron 101 enters orexits the store premise, how long the patron 101 stays in the storepremise, etc. The patron data 109 may further include contact directorydata on the smartphone of the patron 101 where the patron had agreed aspreviously noted, in order to determine the identity and the grouprelations of the patron 101.

The intelligent marketing engine 110 may include a group determinationprocess 120, a cognitive content process 130, patron profiles 140, andthe marketing campaign data 150. The patron profiles 140 store purchasehistories of the patron 101 as associated with group information at thetime of purchases. The marketing campaign data 150 store location events153 and marketing collaterals 157 associated with the location events153. Wherein the patron 101 generates a certain location event in thestore, the intelligent marketing engine 110 determines whether or notthe patron 101 is in a group and a relation for any existing group, andnotifies the patron 101 with the optimal marketing collateral 189, whichis selected from the marketing collaterals 157 associated with thelocation event as most likely for the patron 101 to purchase based ongroup presence. In another embodiment of the present invention, theintelligent marketing engine 110 examines a purchase history in thepatron profiles 140 of the patron 101 in association with a specificgroup presence and selects the optimal marketing collateral 189 based onthe purchase history. In one embodiment of the present invention, thepatron profiles 140 and/or the marketing campaign data 150 may beexternal to the intelligent marketing engine 110 but still communicablevia a network connection etc. Detailed operations of the intelligentmarketing engine 110 are presented in FIG. 2 and correspondingdescription.

The group determination process 120 indicates a functional element ofthe intelligent marketing engine 110 in which the intelligent marketingengine 110 determines whether or not the patron 101 is in a group, and arelationship of the patron 101 to any group that had been determined tobe present. Similarly, the cognitive content process 130 indicatesanother functional element of the intelligent marketing engine 110 inwhich the intelligent marketing engine 110 associates various selectionsof the marketing collaterals 157 to different groups based on cognitiveanalysis of purchase patterns based on respective group purchasehistories, weather, a state of mind for the patron, a state of mind forthe group, date and time of the day, group dynamics, etc., to maximizesales as promoted by the marketing collaterals 157 and to accuratelypredict purchase decisions based on aforementioned factors. Theintelligent marketing engine 110 performs data mining to further improvemarketing efficiency by associating various factors such as grouppresence with purchase patterns as represented in the patron profiles140. The intelligent marketing engine 110 performs data mining toimprove marketing efficiency by associating purchase patterns asrepresented in the patron profiles 140 with a group determination asgenerated by the group determination process 120, cognitive factorssubject to analysis by the cognitive content process 130, etc.

FIG. 2 depicts a flowchart performed by the intelligent marketing engine110 of FIG. 1, in accordance with one or more embodiments set forthherein.

In block 210, the intelligent marketing engine 110 detects a locationevent as generated by movements of a patron in and around a store. Thelocation event is configured to correspond to one or more marketingcollateral in marketing campaign data. Then the intelligent marketingengine 110 proceeds with block 220.

In one embodiment of the present invention, a site/venue employing theintelligent marketing engine 110 for is a retail store, which hasvarious locations within the retail store referred to as a zone such asa dairy zone, a freezer zone, a produce zone, etc. Examples of thelocation event may be, but are not limited to, an entry to a zone, anexit from a zone, a duration of stay in a zone, an entry to an outdoorarea, an exit from an outdoor area, a duration of stay in an outdoorarea, etc. Examples of marketing collaterals may be, but are not limitedto, push text messages to a mobile device of the patron and/or emailmessages to the patron on various in-store promotions such as specialdiscounts, bundled or otherwise incentivized offers, etc.

In block 220, the intelligent marketing engine 110 determines whether ornot the patron is with a group. If the intelligent marketing engine 110determines that the patron is with a group, then the intelligentmarketing engine 110 proceeds with block 230. If the intelligentmarketing engine 110 determines that the patron is without a group, thenthe intelligent marketing engine 110 proceeds with block 250.

In one embodiment of the present invention, the intelligent marketingengine 110 determines a group presence based on proximity and/or travelpattern of multiple mobile devices, etc., based on commerciallyavailable mobile-device based customer support and service systems.

In block 230, the intelligent marketing engine 110 determines arelationship of the group as discovered in block 220, based on variousrelationship data. Examples of distinctive group presences are presentedin FIGS. 3A, 3B and 3C and corresponding description. Then theintelligent marketing engine 110 proceeds with block 240.

In one embodiment of the present invention, the intelligent marketingengine 110 determines the relationship of the group to the patron basedon contact directory data stored in a mobile device of the patron,social network relationships of the patron, and/or other searches basedon the contact directory data of the patron, etc. In certain embodimentsof the present invention, the group relations are selected from apreconfigured types such as family, friends, coworkers, etc.

In another embodiment of the present invention, the intelligentmarketing engine 110 determines the relationship of the group to thepatron based on social media relationship and/or interaction data, whichdirectly or indirectly indicate the relationship.

In another embodiment of the present invention, the intelligentmarketing engine 110 determines the relationship of the group to thepatron based on the patron profiles 140 that is provided to the venuesuch as a form disclosing certain relationships upon registration.

In block 240, the intelligent marketing engine 110 selects an optimalmarketing collateral available for the zone from which the patrongenerated the location event. The intelligent marketing engine 110selects the optimal marketing collateral that the patron in the specificgroup of the relationship as determined in block 230 is most likely topurchase, preconfigured as a respective relationship marketingcollateral. Wherein the marketing campaign data does not include amarketing collateral specifically intended for the type of group, theintelligent marketing engine 110 selects a default marketing collateralfor the zone. Then the intelligent marketing engine 110 proceeds withblock 260.

In one embodiment of the present invention, the intelligent marketingengine 110 may utilize characterization of the group in selecting theoptimal marketing collateral for the patron. For example, theintelligent marketing engine 110 notifies a default marketing collateralif the patron is shopping without a group, and a product generally morepopular with a type of group if the patron is shopping with a group. Inanother embodiment of the present invention, the intelligent marketingengine 110 may utilize the cognitive content process 130 which analyzesa group activity pattern by use of machine learning in selecting theoptimal marketing collateral such that the intelligent marketing engine110 selects the optimal marketing collateral based on purchasehistory/patterns of the patron with the present group, weather, a stateof mind for the patron, a state of mind for the group, date and time ofthe day, group dynamics, etc.

In block 250, the intelligent marketing engine 110 selects a defaultmarketing collateral for the location as the patron is not with a group.Then the intelligent marketing engine 110 proceeds with block 260.

In block 260, the intelligent marketing engine 110 communicates themarketing collateral as selected in previous blocks 240 or 250 to thepatron. Examples of optimal marketing collaterals based on distinctivegroup presences are presented in FIGS. 3A, 3B and 3C and correspondingdescription. Then the intelligent marketing engine 110 terminatesprocessing the location event.

In one embodiment of the present invention, the intelligent marketingengine 110 may further gather and cumulate purchase history of thepatron of a product promoted by the marketing collateral in block 260,and improve effectiveness of future marketing campaigns by use ofcustomized marketing services for respective groups via tools such asthe IBM Marketing Cloud.

FIGS. 3A, 3B and 3C depict examples of marketing collaterals presentedfor a patron in various group scenarios, in accordance with one or moreembodiments set forth herein.

FIG. 3A depicts a first group scenario 300A, in which a patron A walksinto a freezer zone 320 around a freezer 310 of a store area 301 in astore. A store floor 330 indicates the rest of area within the storethat also may have other location/zone set up for intelligent marketing.The intelligent marketing engine 110 determines that the patron A isshopping without a company based on that no other person is presentwithin the freezer zone 320. The intelligent marketing engine 110accordingly selects a default marketing collateral 339 for the freezerzone 320, which is a message promoting ice cream, and sends the defaultmarketing collateral 339 to a mobile device of the patron A. Theintelligent marketing engine 110 may further analyze cognitive factorsand purchase patterns associated with the patron A as an individual,then select another marketing collateral.

FIG. 3B depicts a second group scenario 300B, in which the patron A anda patron B walk into the freezer zone 320. The intelligent marketingengine 110 determines that the patron A is shopping in a group, anddetermines that the patron A and the patron B are family, based oncontact directory data stored in the mobile device of the patron A. Theintelligent marketing engine 110 accordingly selects a family marketingcollateral 349 for the freezer zone 320, which is a message promotingfrozen vegetables, and sends the family marketing collateral 349 to themobile device of the patron A. The intelligent marketing engine 110 mayfurther analyze cognitive factors and purchase patterns associated withthe patrons A and B as a group, then select another family marketingcollateral.

FIG. 3C depicts a third group scenario 300C, in which the patron A, andpatrons C, D, and E walk into the freezer zone 320. The intelligentmarketing engine 110 determines that the patron A is shopping in agroup, and determines that the patrons A, C, D, and E are friends, basedon contact directory data stored in the mobile device of the patron A.By default, a marketing collateral for a group of friends may be afrozen pizza. Instead, the intelligent marketing engine 110 furthersearches for social media postings in which the group of A, C, D, and Eparticipated to determine the optimal marketing collateral for thegroup. The intelligent marketing engine 110 discovers a social mediaposting stating that “I ate too much Halloween candy, time to go on adiet!” by the patron C and analyzes the interactions. According to theanalysis on the state of mind of the group, the intelligent marketingengine 110 determines that members of the group presently support theweight-loss resolution of the patron C, and selects a weight-lossmarketing collateral 359 for the freezer zone 320, which is a messagepromoting fruit salads, and sends the selected weight-loss marketingcollateral 359 to the mobile device of the patron A.

Certain embodiments of the present invention may offer various technicalcomputing advantages, including personalized marketing based on mobiletechnology, particularly group presence and group relations. Certainembodiments of the present invention implement and utilize data miningrelevant to purchase patterns of a patron in a specific types of groupsuch that customized marketing information may be communicated to thepatron for maximum efficacy. Certain embodiments of the presentinvention implement and utilize cognitive analysis of factorsinfluencing purchase behavior or the patron such as group activitypattern, weather, a state of mind for the patron, a state of mind forthe group, date and time, etc. such that the marketing effort would bemore focused on the individual group characteristics and more suitablefor a context of a shopping, in order to achieve maximum utilization ofthe marketing collaterals, which leads to sales increase.

FIGS. 4-6 depict various aspects of computing, including a computersystem and cloud computing, in accordance with one or more aspects setforth herein.

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as Follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as Follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as Follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring now to FIG. 4, a schematic of an example of a computersystem/cloud computing node is shown. Cloud computing node 10 is onlyone example of a suitable cloud computing node and is not intended tosuggest any limitation as to the scope of use or functionality ofembodiments of the invention described herein. Regardless, cloudcomputing node 10 is capable of being implemented and/or performing anyof the functionality set forth hereinabove.

In cloud computing node 10 there is a computer system 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system 12 include, but are not limitedto, personal computer systems, server computer systems, thin clients,thick clients, hand-held or laptop devices, multiprocessor systems,microprocessor-based systems, set top boxes, programmable consumerelectronics, network PCs, minicomputer systems, mainframe computersystems, and distributed cloud computing environments that include anyof the above systems or devices, and the like.

Computer system 12 may be described in the general context of computersystem-executable instructions, such as program processes, beingexecuted by a computer system. Generally, program processes may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program processes may belocated in both local and remote computer system storage media includingmemory storage devices.

As shown in FIG. 4, computer system 12 in cloud computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system 12 may include, but are not limited to, one or moreprocessors 16, a system memory 28, and a bus 18 that couples varioussystem components including system memory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 12 typically includes a variety of computer systemreadable media. Such media may be any available media that is accessibleby computer system 12, and it includes both volatile and non-volatilemedia, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program processes that are configured to carry out thefunctions of embodiments of the invention.

One or more program 40, having a set (at least one) of program processes42, may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram processes, and program data. Each of the operating system, oneor more application programs, other program processes, and program dataor some combination thereof, may include an implementation of theintelligent marketing engine 110 of FIG. 1. Program processes 42, as inthe intelligent marketing engine 110, the group determination process120, and the cognitive content process 130, of FIG. 1, respectively,generally carry out the functions and/or methodologies of embodiments ofthe invention as described herein.

Computer system 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computer system12; and/or any devices (e.g., network card, modem, etc.) that enablecomputer system 12 to communicate with one or more other computingdevices. Such communication can occur via Input/Output (I/O) interfaces22. Still yet, computer system 12 can communicate with one or morenetworks such as a local area network (LAN), a general wide area network(WAN), and/or a public network (e.g., the Internet) via network adapter20. As depicted, network adapter 20 communicates with the othercomponents of computer system 12 via bus 18. It should be understoodthat although not shown, other hardware and/or software components couldbe used in conjunction with computer system 12. Examples, include, butare not limited to: microcode, device drivers, redundant processors,external disk drive arrays, RAID systems, tape drives, and data archivalstorage systems, etc.

Referring now to FIG. 5, illustrative cloud computing environment 50 isdepicted. As shown, cloud computing environment 50 comprises one or morecloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Nodes 10 maycommunicate with one another. They may be grouped (not shown) physicallyor virtually, in one or more networks, such as Private, Community,Public, or Hybrid clouds as described hereinabove, or a combinationthereof. This allows cloud computing environment 50 to offerinfrastructure, platforms and/or software as services for which a cloudconsumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that computing nodes10 and cloud computing environment 50 can communicate with any type ofcomputerized device over any type of network and/or network addressableconnection (e.g., using a web browser).

Referring now to FIG. 6, a set of functional abstraction layers providedby cloud computing environment 50 (FIG. 5) is shown. It should beunderstood in advance that the components, layers, and functions shownin FIG. 6 are intended to be illustrative only and embodiments of theinvention are not limited thereto. As depicted, the following layers andcorresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and processing components for the checkoutoperation optimizer 96, as described herein. The processing components96 can be understood as one or more program 40 described in FIG. 4.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

There is set forth herein, for example: A1. A computer implementedmethod for intelligent marketing, comprising: detecting, by one or moreprocessor, a location event for a location within a venue as generatedby a patron; determining a group of the patron and a relationship of thegroup; selecting an optimal marketing collateral for the patron based onthe group and the relationship from the determining; and communicatingthe optimal marketing collateral from the selecting to the patron suchthat the patron may utilize the optimal marketing collateral. A2. Thecomputer implemented method of A1, the detecting comprising: receivingdata indicating a movement of the patron associated with the location,the movement corresponding to the location event, wherein the locationevent is associated with at least one marketing collateral. A3. Thecomputer implemented method of A2, the determining comprising:ascertaining that the patron is in a group responsive to discoveringthat the patron is in proximity of the group; searching relationshipdata of the patron for members of the group; and identifying therelationship of the group based on the relationship data of the patron.A4. The computer implemented method of A3, the determining comprising:ascertaining that no group of the patron is present when the patrongenerated the location event responsive to discovering that the patrondoes not have anybody nearby; and identifying the relationship of thegroup as a default. A5. The computer implemented method of A4, theselecting comprising: electing the optimal marketing collateral that isassociated with respective relationships of the group of the patron,wherein the default is associated with a default marketing collateral ofthe location, and the respective relationships are associated with arelationship marketing collateral for the location. A6. The computerimplemented method of A1, the selecting further comprising: retrievingat least one cognitive factor relevant to the patron as well as thelocation, wherein the at least one cognitive factor is selected from apurchase history of the patron with the group, weather, state of mindfor the patron, state of mind for the group, date and time of the day,such that the patron is more likely to utilize the optimal marketingcollateral in the venue. A7. The computer implemented method of A1,wherein the optimal marketing collateral is a notification to a mobiledevice of the patron selected from various promotions available from thelocation. B1. A computer program product comprising: a computer readablestorage medium readable by one or more processor and storinginstructions for execution by the one or more processor for performing amethod for intelligent marketing, comprising: detecting, by the one ormore processor, a location event for a location within a venue asgenerated by a patron; determining a group of the patron and arelationship of the group; selecting an optimal marketing collateral forthe patron based on the group and the relationship from the determining;and communicating the optimal marketing collateral from the selecting tothe patron such that the patron may utilize the optimal marketingcollateral. B2. The computer program product of B1, the detectingcomprising: receiving data indicating a movement of the patronassociated with the location, the movement corresponding to the locationevent, wherein the location event is associated with at least onemarketing collateral. B3. The computer program product of B2, thedetermining comprising: ascertaining that the patron is in a groupresponsive to discovering that the patron is in proximity of the group;searching relationship data of the patron for members of the group; andidentifying the relationship of the group based on the relationship dataof the patron. B4. The computer program product of B3, the determiningcomprising: ascertaining that no group of the patron is present when thepatron generated the location event responsive to discovering that thepatron does not have anybody nearby; and identifying the relationship ofthe group as a default. B5. The computer program product of B4, theselecting comprising: electing the optimal marketing collateral that isassociated with respective relationships of the group of the patron,wherein the default is associated with a default marketing collateral ofthe location, and the respective relationships are associated with arelationship marketing collateral for the location. B6. The computerprogram product of B1, the selecting further comprising: retrieving atleast one cognitive factor relevant to the patron as well as thelocation, wherein the at least one cognitive factor is selected from apurchase history of the patron with the group, weather, a state of mindfor the patron, a state of mind for the group, date and time of the day,such that the patron is more likely to utilize the optimal marketingcollateral in the venue. B7. The computer program product of B1, whereinthe optimal marketing collateral is a notification to a mobile device ofthe patron selected from various promotions available from the location.C1. A system comprising: a memory; one or more processor incommunication with memory; and program instructions executable by theone or more processor via the memory to perform a method for intelligentmarketing, comprising: detecting, by the one or more processor, alocation event for a location within a venue as generated by a patron;determining a group of the patron and a relationship of the group;selecting an optimal marketing collateral for the patron based on thegroup and the relationship from the determining; and communicating theoptimal marketing collateral from the selecting to the patron such thatthe patron may utilize the optimal marketing collateral. C2. The systemof C1, the detecting comprising: receiving data indicating a movement ofthe patron associated with the location, the movement corresponding tothe location event, wherein the location event is associated with atleast one marketing collateral. C3. The system of C2, the determiningcomprising: ascertaining that the patron is in a group responsive todiscovering that the patron is in proximity of the group; searchingrelationship data of the patron for members of the group; andidentifying the relationship of the group based on the relationship dataof the patron. C4. The system of C3, the determining comprising:ascertaining that no group of the patron is present when the patrongenerated the location event responsive to discovering that the patrondoes not have anybody nearby; and identifying the relationship of thegroup as a default. C5. The system of C4, the selecting comprising:electing the optimal marketing collateral that is associated withrespective relationships of the group of the patron, wherein the defaultis associated with a default marketing collateral of the location, andthe respective relationships are associated with a relationshipmarketing collateral for the location. C6. The system of C1, wherein theoptimal marketing collateral is a notification to a mobile device of thepatron selected from various promotions available from the location.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting. As used herein, thesingular forms “a,” “an,” and “the” are intended to include the pluralforms as well, unless the context clearly indicates otherwise. It willbe further understood that the terms “comprise” (and any form ofcomprise, such as “comprises” and “comprising”), “have” (and any form ofhave, such as “has” and “having”), “include” (and any form of include,such as “includes” and “including”), and “contain” (and any form ofcontain, such as “contains” and “containing”) are open-ended linkingverbs. As a result, a method or device that “comprises,” “has,”“includes,” or “contains” one or more steps or elements possesses thoseone or more steps or elements, but is not limited to possessing onlythose one or more steps or elements. Likewise, a step of a method or anelement of a device that “comprises,” “has,” “includes,” or “contains”one or more features possesses those one or more features, but is notlimited to possessing only those one or more features. Furthermore, adevice or structure that is configured in a certain way is configured inat least that way, but may also be configured in ways that are notlisted.

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below, if any, areintended to include any structure, material, or act for performing thefunction in combination with other claimed elements as specificallyclaimed. The description set forth herein has been presented forpurposes of illustration and description, but is not intended to beexhaustive or limited to the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the disclosure. Theembodiment was chosen and described in order to best explain theprinciples of one or more aspects set forth herein and the practicalapplication, and to enable others of ordinary skill in the art tounderstand one or more aspects as described herein for variousembodiments with various modifications as are suited to the particularuse contemplated.

What is claimed is:
 1. A computer implemented method, comprising:detecting a location event for a location within a venue as generated bya patron, wherein the patron carries a mobile device, wherein thelocation event specifies a movement of the patron in relation with thelocation including one or more zone associated with respective productsavailable from the venue, and wherein the location event is associatedwith at least one marketing collateral for one of products in a zoneidentified as the location; determining that the patron is in a group asaccompanying one or more person having a relationship with the patron inthe venue based on movements generated by the patron and a rest of thegroup, based on respective location monitoring devices employed in thevenue, and a purchase pattern of the patron that is distinctive based ona type of the relationship between the patron and the rest of the groupbased on dynamic data mining on purchase history of the patron;selecting an optimal marketing collateral for the patron from the atleast one marketing collateral associated with the location event, basedon the group and the relationship between the patron and the rest of thegroup from the determining; and sending the optimal marketing collateralfrom the selecting to the mobile device of the patron such that thepatron utilizes the optimal marketing collateral at the location of thevenue.
 2. The computer implemented method of claim 1, wherein thedetermining comprises ascertaining that the group initially includesonly the patron based on that the patron does not share a travel patternwith other location monitoring devices in the venue; and identifying therelationship of the group.
 3. The computer implemented method of claim1, wherein the selecting comprises retrieving at least one cognitivefactor relevant to the patron as well as the location, wherein the atleast one cognitive factor is based on a purchase history of the patronin the group such that the patron is more likely to purchase a productpromoted by the optimal marketing collateral in the location of thevenue.
 4. The computer implemented method of claim 1, wherein theselecting comprises retrieving at least one cognitive factor relevant tothe patron as well as the location, wherein the at least one cognitivefactor is based on a purchase history of the patron in the group.
 5. Thecomputer implemented method of claim 1, wherein the determiningcomprises ascertaining that the group initially includes only the patronbased on that the patron does not share a travel pattern, wherein theselecting comprises retrieving at least one cognitive factor relevant tothe patron as well as the location.
 6. The computer implemented methodof claim 1, wherein the determining comprises ascertaining that thegroup initially includes only the patron based on that the patron doesnot share a travel pattern; and identifying the relationship of thegroup; wherein the selecting comprises retrieving at least one cognitivefactor relevant to the patron as well as the location, wherein the atleast one cognitive factor is based on a purchase history of the patronin the group.
 7. The computer implemented method of claim 1, thedetecting comprising: receiving, from the mobile device by way of one ofthe location monitoring devices in the venue, data indicating themovement of the patron corresponding to the location event, wherein thelocation event is selected from the group consisting of an entry to thelocation, an exit from the location, and a duration of stay in thelocation, wherein the location further includes an outdoor area of thevenue.
 8. The computer implemented method of claim 1, the determiningfurther comprising: ascertaining that the patron is in the group,responsive to subsequently discovering that the patron is in proximityof the rest of the group, and that the patron and the rest of the groupshare a travel pattern in the venue, based on similar location events asgenerated by the respective location monitoring devices on the patronand the rest of the group in the venue; searching relationship data ofthe patron for members of the rest of the group, wherein therelationship data is based on dynamic data mining selected from thegroup consisting of: contact directory data stored in the mobile deviceof the patron; and social network relationship data; and identifying therelationship of the group based on the relationship data of the patron.9. The computer implemented method of claim 1, the selecting furthercomprising: electing the optimal marketing collateral for the productthat is most likely to be purchased by the patron while being with therest of the group of respective relationships to the patron, wherein adefault relationship is associated with a default marketing collateralof the location, indicating that the patron is at the venue as anindividual, and the respective relationships are associated with arelationship marketing collateral for the location.
 10. The computerimplemented method of claim 1, wherein the selecting comprisesretrieving at least one cognitive factor relevant to the patron as wellas the location, wherein the at least one cognitive factor is selectedfrom the group consisting of the purchase history of the patron with thegroup, weather, state of mind for the patron, state of mind for thegroup, and date and time of the day.
 11. The computer implemented methodof claim 1, wherein the optimal marketing collateral is selected fromthe group consisting of a notification to the mobile device of thepatron including a promotion for the product that the patron is mostlikely to purchase in the presence of the group, and an email message tothe patron informing the promotion.
 12. A computer program productcomprising: a storage medium readable by one or more processing circuitand storing instructions for execution by one or more processor forperforming a method comprising: detecting a location event for alocation within a venue as generated by a patron, wherein the patroncarries a mobile device, wherein the location event specifies a movementof the patron in relation with the location including one or more zoneassociated with respective products available from the venue, andwherein the location event is associated with at least one marketingcollateral for one of products in a zone identified as the location;determining that the patron is in a group as accompanying one or moreperson having a relationship with the patron in the venue based onmovements generated by the patron and a rest of the group, based onrespective location monitoring devices employed in the venue, and apurchase pattern of the patron that is distinctive based on a type ofthe relationship between the patron and the rest of the group based ondynamic data mining on purchase history of the patron; selecting anoptimal marketing collateral for the patron from the at least onemarketing collateral associated with the location event, based on thegroup and the relationship between the patron and the rest of the groupfrom the determining; and sending the optimal marketing collateral fromthe selecting to the mobile device of the patron such that the patronutilizes the optimal marketing collateral at the location of the venue.13. A system comprising: a memory; one or more processor incommunication with the memory; and program instructions executable bythe one or more processor via the memory to perform a method comprising:detecting a location event for a location within a venue as generated bya patron, wherein the patron carries a mobile device, wherein thelocation event specifies a movement of the patron in relation with thelocation including one or more zone associated with respective productsavailable from the venue, and wherein the location event is associatedwith at least one marketing collateral for one of products in a zoneidentified as the location; determining that the patron is in a group asaccompanying one or more person having a relationship with the patron inthe venue based on movements generated by the patron and a rest of thegroup, based on respective location monitoring devices employed in thevenue, and a purchase pattern of the patron that is distinctive based ona type of the relationship between the patron and the rest of the groupbased on dynamic data mining on purchase history of the patron;selecting an optimal marketing collateral for the patron from the atleast one marketing collateral associated with the location event, basedon the group and the relationship between the patron and the rest of thegroup from the determining; and sending the optimal marketing collateralfrom the selecting to the mobile device of the patron such that thepatron utilizes the optimal marketing collateral at the location of thevenue.
 14. The system of claim 13, wherein the determining comprisesascertaining that the group initially includes only the patron based onthat the patron does not share a travel pattern with other locationmonitoring devices in the venue; and identifying the relationship of thegroup.
 15. The system of claim 13, wherein the selecting comprisesretrieving at least one cognitive factor relevant to the patron as wellas the location, wherein the at least one cognitive factor is based on apurchase history of the patron in the group such that the patron is morelikely to purchase a product promoted by the optimal marketingcollateral in the location of the venue.
 16. The system of claim 13,wherein the selecting comprises retrieving at least one cognitive factorrelevant to the patron as well as the location, wherein the at least onecognitive factor is based on a purchase history of the patron in thegroup.
 17. The system of claim 13, wherein the determining comprisesascertaining that the group initially includes only the patron based onthat the patron does not share a travel pattern, wherein the selectingcomprises retrieving at least one cognitive factor relevant to thepatron as well as the location.
 18. The system of claim 13, wherein thedetermining comprises ascertaining that the group initially includesonly the patron based on that the patron does not share a travelpattern; and identifying the relationship of the group; wherein theselecting comprises retrieving at least one cognitive factor relevant tothe patron as well as the location, wherein the at least one cognitivefactor is based on a purchase history of the patron in the group. 19.The system of claim 13, wherein the selecting comprises retrieving atleast one cognitive factor relevant to the patron as well as thelocation, wherein the at least one cognitive factor comprises each ofthe purchase history of the patron with the group, weather, state ofmind for the patron, state of mind for the group, and date and time ofthe day.
 20. The system of claim 13, wherein the optimal marketingcollateral is selected from the group consisting of a notification tothe mobile device of the patron including a promotion for the productthat the patron is most likely to purchase in the presence of the group,and an email message to the patron informing the promotion.